论文标题
联合学习实例和语义细分，用于机器人拾取和杂乱的重闭。
Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter
论文作者
论文摘要
我们介绍了实例学习和语义分割的共同学习，以了解可见的和遮挡的区域面具。与实例遮挡分割共享特征提取器，我们将语义遮挡分割引入实例分割模型。这种联合学习融合了对不同分割任务的掩码预测的实例和图像级推理，这仅在以前的学习实例分割的工作中缺少（仅实例）。在实验中，我们评估了提出的联合学习，比较了测试数据集中仅实例学习的研究。我们还将联合学习模型应用于2种不同类型的机器人拾取任务（随机和目标拾取），并评估了其实现现实世界机器人任务的有效性。
We present joint learning of instance and semantic segmentation for visible and occluded region masks. Sharing the feature extractor with instance occlusion segmentation, we introduce semantic occlusion segmentation into the instance segmentation model. This joint learning fuses the instance- and image-level reasoning of the mask prediction on the different segmentation tasks, which was missing in the previous work of learning instance segmentation only (instance-only). In the experiments, we evaluated the proposed joint learning comparing the instance-only learning on the test dataset. We also applied the joint learning model to 2 different types of robotic pick-and-place tasks (random and target picking) and evaluated its effectiveness to achieve real-world robotic tasks.
